A Hybrid Social Network-based Collaborative Filtering Method for Personalized Manufacturing Service Recommendation

International journal of computers, communications and control

Wenting Yang¹  Wenyu Zhang¹  Song Xu²  Shuai Zhang² 

[1] Economics, China;Zhejiang University of Finance &

关键词: manufacturing service recommendation;    social network;    collaborative filtering;    SALSA;    PSO;   

DOI  :  10.15837/ijccc.2017.5.2930

学科分类：计算机科学（综合）

来源: Universitatea Agora

【 摘 要 】

Nowadays, social network-based collaborative filtering (CF) methods are widely applied to recommend suitable products to consumers by combining trust relationships and similarities in the preference ratings among past users. However, these types of methods are rarely used for recommending manufacturing services. Hence, this study has developed a hybrid social network-based CF method for recommending personalized manufacturing services. The trustworthy enterprises and three types of similar enterprises with different features were considered as the four influential components for calculating predicted ratings of candidate services. The stochastic approach for link structure analysis (SALSA) was adopted to select top K trustworthy enterprises while also considering their reputation propagation on enterprise social network. The predicted ratings of candidate services were computed by using an extended user-based CF method where the particle swarm optimization (PSO) algorithm was leveraged to optimize the weights of the four components, thus making service recommendation more objective. Finally, an evaluation experiment illustrated that the proposed method is more accurate than the traditional user-based CF method.

【 授权许可】

Free